Adaptive Gamification for Learning Environments

(Scimago Q1, ATIEF A+)International audienceIn spite of their effectiveness, learning environments often fail to engage users and end up under-used. Many studies show that gamification of learning environments can enhance learners' motivation to use learning environments. However, learners react differently to specific game mechanics and little is known about how to adapt gaming features to learners' profiles. In this paper, we propose a process for adapting gaming features based on a player model. This model is inspired from existing player typologies and types of gamification elements. Our approach is implemented in a learning environment with five different gaming features, and evaluated with 266 participants. The main results of this study show that, amongst the most engaged learners (i.e. learners who use the environment the longest), those with adapted gaming features spend significantly more time in the learning environment. Furthermore, learners with features that are not adapted have a higher level of amotivation. These results support the relevance of adapting gaming features to enhance learners' engagement, and provide cues on means to implement adaptation mechanisms

Gamification and player profiles among faculty in Mexico

Objectives: Analysis of the player profiles of professors is a fruitful line of research because player profiles may influence the design of gamified situations. We studied a sample of 243 university professors in Mexico to analyze the player profiles with which they identify and those they consider most effective didactically in gamified situations. Method: Descriptive quantitative research was used to analyze the distributions of the responses to a questionnaire given to a group of 243 professors from different Mexican universities. These responses have been statistically analyzed by computing the proportions of player profile choices and applying Pearson’s chi-square test of independence to identify significant differences in these choices. Results: 42.4% of the participants identify as Explorers, the most frequent player profile among the participants. However, about 15.6% of them consider that their player profile is not the most suitable for learning. Player profiles chosen by the Mexican professors diverge from the player profiles of the students described in previous studies. Significant differences by gender, area of knowledge, and previous training in gamification are also identified. Conclusion: There is a strong gap between the player profiles of the participating professors and the profile that, in their opinion, is most suitable for learning. In addition, it has been identified that gender, area of knowledge, and previous experience in the use of gamification are influential factors in the player profiles of the professors. Implication for Practice: The training of professors in gamification should be adapted to the specificities of each area of knowledge. This will allow professors to develop pedagogical skills in gamification that will help them adapt gamified didactic situations to the needs of students

Generic physiological features as predictors of player experience

This paper examines the generality of features extracted from heart rate (HR) and skin conductance (SC) signals as predictors of self-reported player affect expressed as pairwise preferences. Artificial neural networks are trained to accurately map physiological features to expressed affect in two dissimilar and independent game surveys. The performance of the obtained affective models which are trained on one game is tested on the unseen physiological and self-reported data of the other game. Results in this early study suggest that there exist features of HR and SC such as average HR and one and two-step SC variation that are able to predict affective states across games of different genre and dissimilar game mechanics.peer-reviewe

Convergence of Gamification and Machine Learning: A Systematic Literature Review

Recent developments in human–computer interaction technologies raised the attention towards gamification techniques, that can be defined as using game elements in a non-gaming context. Furthermore, advancement in machine learning (ML) methods and its potential to enhance other technologies, resulted in the inception of a new era where ML and gamification are combined. This new direction thrilled us to conduct a systematic literature review in order to investigate the current literature in the field, to explore the convergence of these two technologies, highlighting their influence on one another, and the reported benefits and challenges. The results of the study reflect the various usage of this confluence, mainly in, learning and educational activities, personalizing gamification to the users, behavioral change efforts, adapting the gamification context and optimizing the gamification tasks. Adding to that, data collection for machine learning by gamification technology and teaching machine learning with the help of gamification were identified. Finally, we point out their benefits and challenges towards streamlining future research endeavors.publishedVersio

Electronic sports and traditional sports:a comparative analysis

Abstract. This study studies the relationship between electronic sports and traditional sports in terms of economic characteristics. In recent years, electronic sports or eSports are gradually viewed as a nascent industry. The exponential growth of electronic sports has led to several studies analyzing its relation to traditional sports. In this thesis, their relationship is reviewed under economic terms. In the first chapter, industry background and history of eSports is provided. Next, the growth rate is presented in terms of revenue, audience base, prize pool, and consumer awareness. The three future scenarios of eSports are then introduced. Electronic sports are predicted to take one of the three forms: “as a counterculture or alternative to the modern sport, as part of the hegemony of sport or as the future hegemonic sport”. The feasibility of each scenario is then evaluated, and among the three, the second scenario — as part of the hegemony of sport is considered the most plausible option. The second part of this thesis deals with the previous literature review in the field of the traditional sport. By applying the same standards and theoretical approaches, electronic sports are put under the same examination. The purpose of this chapter is to provide the structure of electronic sport by segments and types of services/ goods that eSports offer to consumers. Prior research in economics analysis on sport and recreation have classified traditional sport as a commodity. Electronic sport, in the same manner, can also be viewed as a commodity. In chapter three, economic characteristics of traditional sports and eSports are compared. One important finding that can be found in this part is the socio-economic factors that affect consumer demand for eSports. On analyzing the determinants of demand for eSports, age, gender, income, employment status are factors with the most influential impact. Time, on the other hand, is regarded as a constraint. In the last part of the thesis, by imposing the two-stage model used by sports economists, the impact of determinants of demand on eSport participation and participation frequency are examined. To gather the data, an online survey is created, and the data is analyzed by using SPSS software (version 25.0). The result resemblances those of traditional sports in prior studies, marking the similarities between the two. One novel finding is the distinction between factors affecting demand for gaming and for eSports. Such a result rules out the inherent stigma of eSports being interchangeable to gaming. In fact, the result strengthens the previous prediction of electronic sports as part of the hegemony of sport. The thesis ends with a summary of findings, limitations of this research, and the call for further research

Quizbowl: Success In and Out of the Classroom, a Five Year Study

Scores of US and Canadian universities' undergraduate students participate in the SS-AAEA Quiz-bowl competition annually. Surveys of the 2001 through 2005 competition participants suggest how beneficial competition preparation and participation are in completing related university work and indicate factors which enhance chances of success in the competition.Teaching/Communication/Extension/Profession,

Identifying Player Types to Tailor Game-Based Learning Design to Learners:Cross-sectional Survey using Q Methodology

BACKGROUND: Game-based learning appears to be a promising instructional method because of its engaging properties and positive effects on motivation and learning. There are numerous options to design game-based learning; however, there is little data-informed knowledge to guide the choice of the most effective game-based learning design for a given educational context. The effectiveness of game-based learning appears to be dependent on the degree to which players like the game. Hence, individual differences in game preferences should be taken into account when selecting a specific game-based learning design. OBJECTIVE: We aimed to identify patterns in students' perceptions of play and games-player types and their most important characteristics. METHODS: We used Q methodology to identify patterns in opinions on game preferences. We recruited undergraduate medical and dental students to participate in our study and asked participants to sort and rank 49 statements on game preferences. These statements were derived from a prior focus group study and literature on game preferences. We used by-person factor analysis and varimax rotation to identify common viewpoints. Both factors and participants' comments were used to interpret and describe patterns in game preferences. RESULTS: From participants' (n=102) responses, we identified 5 distinct patterns in game preferences: the social achiever, the explorer, the socializer, the competitor, and the troll. These patterns revolved around 2 salient themes: sociability and achievement. The 5 patterns differed regarding cheating, playing alone, story-telling, and the complexity of winning. CONCLUSIONS: The patterns were clearly interpretable, distinct, and showed that medical and dental students ranged widely in how they perceive play. Such patterns may suggest that it is important to take students' game preferences into account when designing game-based learning and demonstrate that not every game-based learning-strategy fits all students. To the best of our knowledge, this study is the first to use a scientifically sound approach to identify player types. This can help future researchers and educators select effective game-based learning game elements purposefully and in a student-centered way

Gamification of e-Learning: an investigation into the influence of gamification on student motivation.

Master of Commerce in Information Systems & Technology. University of KwaZulu-Natal, Durban, 2017.Traditional teacher-centred learning is being confronted by an increasing awareness of the value of student-centred learning. E-learning, despite its limitations, is often presented as a solution to learning challenges prevalent in teacher-centred learning since it affords students greater control of the learning process. Combined with this, academics are increasingly competing for students’ attention and struggle to motivate students. However, students, when confronted with the array of games and social media platforms available, willingly dedicate several hours glued to their screens socialising, engaging and gaming. Such willingness to engage these so-called distractions whilst displaying reluctance to engage their academic work may be attributed to a lack of motivation. This is even more prevalent in the domain of e-learning. Adopting an embedded mixed methods case study design, this study explored the influence of gamification of e-learning on motivation. Herein, expectations and factors influencing experiences of gamification of e-learning were explored. Furthermore, through Self-Determination Theory (SDT) & Intrinsic Motivation Inventory (IMI) as theoretical lenses, this study explored how gamification of e-learning influences motivation. Gamification is conceptualised as an objective-driven user-centred technique which integrates game mechanics, dynamics and game aesthetics into real-world contexts to motivate behaviour. Gartner envisages that by 2020, gamification will be deeply integrated into the prevalent higher education structures. Whilst many applications of gamification aim towards enhancing classroom-based learning, the exploration of gamification of e-learning in higher education, particularly in a developing country, remains an emerging domain of research. This research found that participants experienced gamification and various game elements differently, based on their BrainHex gamer profiles. In terms of SDT, whilst progression through the gamified course was guided and consistent, with all participants progressing as a single group, they experienced a sense of autonomy. Participants also experienced a greater sense of competence and relatedness in engaging with the gamified course. In the context of IMI, participants’ experiences suggest that gamification was valuable, increased curiosity and was effective for learning. However, they reported experiencing tension and a high degree of effort required by the gamified course. Students expected transparency in terms of scoring and raised queries where required. They generally preferred visual cues whilst engaging with the gamified course, expected almost real-time feedback in terms of scoring and resolution of queries, but had varying views on which game elements motivated them. Essentially, it was found that gamification positively influenced participants’ motivation. However, it must be noted that whilst gamification motivated students, some experienced demotivation. Contributing factors include not understanding the game from the outset, being demotivated by not earning frequent rewards and losing progress in the game due to external factors

Assessing Influential Users in Live Streaming Social Networks

abstract: Live streaming has risen to significant popularity in the recent past and largely this live streaming is a feature of existing social networks like Facebook, Instagram, and Snapchat. However, there does exist at least one social network entirely devoted to live streaming, and specifically the live streaming of video games, Twitch. This social network is unique for a number of reasons, not least because of its hyper-focus on live content and this uniqueness has challenges for social media researchers. Despite this uniqueness, almost no scientific work has been performed on this public social network. Thus, it is unclear what user interaction features present on other social networks exist on Twitch. Investigating the interactions between users and identifying which, if any, of the common user behaviors on social network exist on Twitch is an important step in understanding how Twitch fits in to the social media ecosystem. For example, there are users that have large followings on Twitch and amass a large number of viewers, but do those users exert influence over the behavior of other user the way that popular users on Twitter do? This task, however, will not be trivial. The same hyper-focus on live content that makes Twitch unique in the social network space invalidates many of the traditional approaches to social network analysis. Thus, new algorithms and techniques must be developed in order to tap this data source. In this thesis, a novel algorithm for finding games whose releases have made a significant impact on the network is described as well as a novel algorithm for detecting and identifying influential players of games. In addition, the Twitch network is described in detail along with the data that was collected in order to power the two previously described algorithms.Dissertation/ThesisDoctoral Dissertation Computer Science 201